Adaptive Storage Scheduling via Q-Learning for SSD Performance
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Solution Overview
Problem
Storage devices, such as SSDs, face inefficiencies in scheduling background operations like garbage collection, which can delay response times and deteriorate system performance due to fixed timing schedules, not adapting to dynamic operating conditions.
Innovation Solution
Implementing a method that uses a machine learning algorithm, specifically Q-learning, to adaptively schedule background operations based on target and existing values of operating parameters related to both the storage device and host, determining optimal timing for operations like garbage collection to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed timing schedule is used for background operations, then device complexity is reduced, but response time deteriorates and performance decreases
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed, static scheduling schedule to a dynamic adaptive schedule generated through machine learning. The Q-learning algorithm continuously learns optimal timing for background operations based on real-time system states and host conditions, allowing the scheduling mechanism to adapt flexibly to changing conditions while maintaining manageable complexity through automated decision-making.
Solution Approach 2:
The patent implements feedback by using Q-learning, which continuously monitors system performance and host conditions, then adjusts the background operation schedule accordingly. The algorithm learns from past decisions and outcomes, incorporating feedback loops that optimize response time by adapting the schedule based on actual system behavior and host workload patterns.
2Loss of time
If adaptive machine learning scheduling is implemented, then response time and performance are improved, but device complexity increases
Solution Approach 1:
The patent applies self-service by implementing an autonomous Q-learning algorithm that automatically generates and adjusts its own scheduling decisions without requiring manual intervention or complex external control systems. The machine learning model self-adapts to optimal scheduling strategies by learning from system feedback, thereby improving response time while managing complexity through autonomous operation.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting scheduling parameters based on learned patterns from system state and host condition data. The Q-learning algorithm modifies timing parameters, priority levels, and operation scheduling based on changing conditions, enabling improved response time through adaptive parameter optimization rather than fixed configurations.
3Reliability
If background operations are prioritized, then storage device maintenance is improved, but host operation performance deteriorates due to resource contention
Solution Approach 1:
The patent applies dynamics by using Q-learning to dynamically balance background operation scheduling against host workload conditions. The algorithm adjusts the timing and priority of maintenance operations based on real-time system state and host performance metrics, ensuring storage reliability through regular maintenance while minimizing disruption to host operations by adapting to actual workload demands.
Solution Approach 2:
The patent implements partial action by scheduling background operations at optimized intervals rather than continuously or at fixed high frequency. The Q-learning algorithm determines the appropriate level and timing of maintenance actions based on system needs and host conditions, providing sufficient storage maintenance while avoiding excessive operations that would contend for resources and degrade host performance.
Data Source
AI summary
A method of operating a storage device according to an exemplary embodiment includes receiving a first target value and a second target value of a plurality of target values respectively corresponding to a first operating parameter and a second operating parameter of a plurality of target values of operating parameters from a host, loading a first existing value and a second existing value of a plurality of existing values of the first operating parameter and the second operating parameter, processing a machine learning algorithm using the first target value, the second target value, the first existing value and the second existing value to generate an adaptive schedule, and executing the background operation based on the schedule.